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Related Concept Videos

Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Related Experiment Video

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Advancing Hyperspectral Targeted Alpha Therapy with Adversarial Machine Learning.

Jim Zhao, Greg Leadman

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    |April 1, 2024
    PubMed
    Summary

    Hyper-spectral Single Photon Imaging (HSPI) offers real-time monitoring for Targeted Alpha Therapy (TAT). This new framework precisely tracks alpha particles, optimizing cancer treatment delivery.

    Area of Science:

    • Medical Physics
    • Radiochemistry
    • Oncology

    Background:

    • Targeted Alpha Therapy (TAT) shows promise for cancer treatment due to the high energy of alpha particles.
    • Effective delivery and real-time monitoring are critical for optimizing TAT efficacy and minimizing off-target effects.

    Approach:

    • A novel Hyper-spectral Single Photon Imaging (HSPI) framework was developed for real-time in vivo assessment of TAT.
    • The system integrates spectral unmixing, quantitative dosimetry, and spatiotemporal visualization capabilities.
    • Advanced spectral unmixing algorithms and adversarial machine learning were employed to discriminate alpha-induced signals from background noise.

    Key Points:

    • The developed HSPI system is specifically designed for alpha-emitting radionuclides.
    • It enables simultaneous acquisition of high-resolution spectral data and single-photon localization.

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  • Precise alpha particle tracking was achieved by distinguishing alpha-induced scintillation from background fluorescence.

    • Conclusions:

    • This HSPI framework provides a comprehensive solution for monitoring and optimizing TAT.
    • The technology facilitates accurate spatiotemporal visualization and dosimetry for improved cancer therapy.
    • HSPI represents a significant advancement in real-time assessment of alpha-particle-based treatments.